Search Results (4,373)

Compound flash drought–heatwave extremes (FDHW) expose vegetation to rapid water and heat stress, but regional assessments often conflate event detection with vegetation response and rarely resolve delayed canopy trajectories. We quantified FDHW across China’s Northeast Black Soil Region during the 1995–2024 growing seasons using ERA5-Land soil-moisture and temperature thresholds, applied a spatiotemporal graph neural network to regularize threshold-derived event masks, and reserved AVHRR NDVI for independent post-event impact assessment. Flash drought and FDHW frequencies exhibited strong interannual variability rather than a significant monotonic trend. FDHW occurrence increased from 3.8 to 4.8 d per growing season between 1995–2005 and 2016–2024, but the Theil–Sen trend was near zero (0.05 d per decade). Land–atmosphere composites indicate progressive soil-moisture depletion before FDHW occurrence and a transition from latent to sensible heat release roughly three days before maximum temperature anomalies. NDVI composites revealed a delayed greenness response: anomalies were negative through the first two post-event weeks, reached their minimum approximately one week after the reference FDHW grid-day, and then partially recovered during days 16–30. Mean NDVI suppression was modest (short-term −0.009; long-term −0.006), but persistent negative anomalies remained in 12.1% of southern cropland-dominant trajectories and 10.7% of northern forest–crop ecotone trajectories. These results show that FDHW impacts in the NBSR are expressed less as a steady rise in event frequency than as delayed and spatially heterogeneous vegetation stress, highlighting the need for post-event monitoring windows and cross-sensor validation to support agricultural risk assessment and adaptation planning. Full article

Wetlands and surface-water features regulate the thermal environment of cities through evaporative cooling, yet in arid metropolitan regions these hydrological buffers are scarce and rarely quantified against urban heat. Here, we link satellite-derived surface-water wetness to land surface temperature (LST) and urban heat island (UHI) intensity in Riyadh, Saudi Arabia, using an explainable Geospatial Artificial Intelligence (GeoAI) framework. We assembled 2000 cloud-masked Landsat 8/9 sample points for July 2014 and 2024 in Google Earth Engine and derived the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Built-up Index (NDBI), and two surface-water indices, the Modified Normalized Difference Water Index (MNDWI) and the Normalized Difference Water Index (NDWI), together with LST, UHI, terrain and population. Surface-water wetness was the strongest cool-side correlate of thermal stress: MNDWI related negatively to LST (r = −0.48) and to UHI intensity (r = −0.53), stronger than either vegetation or built-up density (both p < 0.001). Each 0.1 increase in MNDWI corresponded to a 2.2 °C reduction in LST. Five machine-learning algorithms predicted LST with test R² of 0.71–0.76 and UHI with R² of 0.68–0.72, and SHapley Additive exPlanations (SHAPs) identified MNDWI as the single most important thermal driver, ahead of elevation and vegetation. Point-level LST rose by 1.99 °C between 2014 and 2024 (p < 0.001), while open surface water was absent from all 2000 samples, indicating a hydrological deficit in the city’s thermal regulation. These findings suggest that protecting and expanding blue–green features along corridors such as Wadi Hanifah offers a measurable cooling lever for arid-city climate adaptation. Full article

Urban Green Spaces (UGSs) monitoring usually focuses on the extraction of vegetation in the physical layer, while neglecting their functional attributes. This renders the monitoring results unable to objectively reflect the rationality of UGS planning. To address these issues, this study proposes a multi-level classification method integrating multi-source remote sensing and geospatial big data to bridge the semantic gap between the physical layer and the functional layer. In this method, a strategy of prior knowledge injection and semantic reconstruction was developed through the fine-tuning of a BERT model with cross-mapping rules. This strategy aims to classify the urban area into 24 functional categories, generating the social-functional basemap in a functional layer, based on Point of Interest (POI), OpenStreetMap (OSM), and Global Urban Boundary (GUB). Meanwhile, a novel deep learning architecture, namely the Multi-Shape and Spectral Aware Network (MSSANet), was designed for precise vegetation classification of UGSs in the physical layer. Finally, a “function-first, vegetation-second” coupling paradigm containing three functional attribute layers, referring to the Code for Classification of UGS in China (CJJ/T 85-2017), was established. This paradigm integrates the social-functional basemap with physical vegetation patches to build a multi-level UGS classification framework, i.e., the 5 major UGS categories, 11 intermediate UGS categories, and 24 fine-grained UGS sub-categories. Experiments conducted in Jinan and Qingdao, China, demonstrate the efficacy of the proposed method for refined multi-level UGS mapping. Full article

Urban plant communities are widely regarded as important nature-based solutions for climate mitigation, yet their actual carbon benefits remain uncertain: vegetation growth is accompanied by carbon emissions from construction and long-term maintenance, and existing assessments rarely integrate community succession, interspecific competition, and maintenance-related emissions within a consistent life-cycle framework. To address these limitations, this study developed a dynamic succession-based life-cycle simulation model to project the 50-year carbon source–sink transitions of 150 typical urban plant communities in Tianjin, China. The model updates plant structural attributes—diameter at breast height, crown width, and tree height—iteratively by linking individual plant growth to environmental suitability and neighborhood competition through a Plant Health Index. Simulated structural trajectories were coupled with biomass equations and carbon content coefficients to estimate aboveground carbon sequestration, while construction and maintenance emissions were quantified using life cycle assessment, enabling evaluation of modeled net carbon balance rather than gross carbon sequestration alone. Under the modeled 50-year scenario, most communities were projected to act as carbon sources during the early stage but gradually shifted toward carbon sinks as biomass accumulated; 86.1% of the communities were projected to become net carbon sinks after 50 years (a scenario-based projection under specified growth, maintenance, and emission assumptions). The highest modeled net carbon balance reached 3186.08 kg C ha⁻¹, whereas the weakest community remained a slight carbon source at −81.21 kg C ha⁻¹. Vertical structural complexity and species richness were the strongest positive predictors of modeled net carbon balance, followed by three-dimensional green quantity and canopy closure. Among maintenance processes, fertilization was the dominant emission source, followed by pesticide application and irrigation; comparative scenario analysis showed that resource-saving maintenance consistently improved projected net carbon balance relative to high-maintenance management. These results suggest that low-carbon planting design should prioritize locally adapted species, multi-layered vertical structures, and adaptive maintenance over simply maximizing planting density or minimizing inputs. The results represent scenario-based projections of aboveground vegetation carbon balance; belowground biomass, soil carbon, litter carbon, dead organic matter, and parameter uncertainty were not fully incorporated, and future studies should address these limitations to improve the robustness and transferability of the proposed framework. Full article

Early-generation sesame (Sesamum indicum L.) breeding requires high-throughput phenotyping of large unreplicated populations across contrasting environments. A DJI Phantom 4 Multispectral UAV was flown at 40, 80, and 120 m above ground level (AGL) over 588 M₂ genotypes under full irrigation (ENV1) and terminal drought (ENV2; irrigation withheld from reproductive onset) on four dates (July–September 2025). Structure-from-motion canopy height models were compared with ground measurements, and four spectral reflectance indices—Normalised Difference Vegetation Index (NDVI), Normalised Difference Red Edge (NDRE), Green Normalised Difference Vegetation Index (GNDVI), and Leaf Chlorophyll Index (LCI)—were derived from 40 m imagery. Ordinary least squares (OLS), Random Forest, and Gradient Boosting were evaluated under leave-one-genotype-out (LOGO), leave-one-environment-out (LOEO), and leave-one-date-out (LODO) cross-validation; genotypic repeatability was quantified by intraclass correlation (ICC), and drought performance was ranked by a composite Stress Tolerance Index (STI) validated against an independent breeder assessment. The 40 m altitude gave the highest height accuracy (R² = 0.812 in ENV1; 0.663 in ENV2). LOGO accuracy (R² ≈ 0.83) fell to R² ≈ 0.55 under LODO—the operationally relevant figure for a new phenological stage—and the full structural–spectral OLS model collapsed (R² = −0.203) where tree ensembles remained stable. Spectral-index repeatability was up to ~2-fold higher under stress (ICC(3,4) > 0.84). The composite STI flagged 38 elite genotypes (7.6% of 498); 10 of its top 30 were confirmed in the breeder’s 48-best selection from all 588 rows—a 4.1-fold enrichment over chance (hypergeometric p = 4.5 × 10⁻⁵). Full article

Although climate-smart agricultural practices are increasingly promoted, comparative environmental and economic assessments across multiple practices and crops remain limited. This study evaluates five climate-smart agricultural practices in Dutch potato and onion production, including soil management, biodiversity enhancement, sustainable irrigation systems, crop protection, and green energy use. It compares them with conventional production systems using integrated Life Cycle Assessment and Life Cycle Costing. Specifically, Life Cycle Assessment (LCA) and Life Cycle Costing (LCC) methodologies were applied to assess the environmental and economic sustainability of the studied systems, respectively. Among the evaluated practices, soil management exhibited the best overall environmental performance for both crops, achieving reductions of up to 42% and 66% in greenhouse gas emissions for potatoes and onions, respectively, compared with the baseline under the modelled conditions. Biodiversity measures significantly reduced freshwater eutrophication and ecotoxicity-related impacts, particularly in potato cultivation, while crop protection practices mainly improved pesticide-related toxicity categories. Similarly, soil management and biodiversity demonstrated the best economic performance, with profits increasing to approximately €3318/ha and €3121/ha for potatoes and €3898/ha and €3694/ha for onions, respectively, compared with baseline profits of €2879/ha and €3526/ha. The results suggest that the implementation of CSA practices can improve both the environmental and economic sustainability of intensive vegetable production systems under the modelled Dutch conditions, although the effectiveness of each practice depends strongly on crop-specific environmental hotspots and management assumptions. The findings provide evidence to support farmers and policymakers in selecting cost-effective climate-smart practices while identifying priorities for future field validation and uncertainty assessment. Full article

Urbanization is a major driver of land-use change and ecological shifts, especially in semi-arid regions with high environmental sensitivity. This study examined urban land growth and its ecological impacts in Astana, Kazakhstan, from 2000 to 2020 and forecasted trends for 2030. Landsat imagery was classified using a Support Vector Machine (SVM) approach, and ecological conditions were assessed through spectral indices, including Normalized Difference Vegetation Index (NDVI), land surface temperature (LST), a Tasseled Cap Wetness index (Wet), and a Normalized Difference Bare-Soil and Built-up Index (NDBSI). The Future Land Use Simulation (CA–Markov) model simulated land use under Business-as-Usual (BAU) and Ecological Priority (EP) scenarios. The results showed a significant increase in built-up land, mainly at the expense of cropland and grassland, with increased landscape fragmentation and rising LST, indicating intensifying urban heat. Ecological indices showed spatially varied responses, with localized greening in protected areas and overall environmental pressure in expanding zones. Scenario simulations suggest that policy interventions under the EP scenario can mitigate cropland loss, limit fragmentation, and enhance ecological connectivity compared with BAU. Overall, the findings show that integrating remote sensing, machine learning, and scenario modeling offers an effective framework for assessing urban–ecological dynamics and supports evidence-based planning for sustainable urban development in semi-arid cities. Full article

With rapid urbanization, the generation of municipal solid waste is growing, placing ever-increasing pressure on cities to close, remediate and repurpose landfill sites in environmentally sustainable and climate-adaptive ways. Traditional landfill final covers such as compacted clay and geosynthetic systems are intended to limit infiltration; yet their conceptual designs often fail in performance longevity due to effects such as desiccation, settlement, root intrusion, freeze–thaw cycling and extreme rainfall. Phytocapping, or evapotranspiration/store-and-release cover technology is the use of vegetated soil profiles to provide storage for percolating rainfall, return water to the atmosphere through evapotranspiration and support biologically mediated oxidation of methane. Phytocapping is a green-inclusive nature-based climate adaptation strategy for urban landfill closure. This study explores hydrological performance, methane mitigation, ecological co-benefits, economic feasibility, climate sensitivity, monitoring requirements and regulatory barriers linked to phytocapping systems. Field evidence is strongest in Australia and the United States, especially through ACAP- and A-ACAP-style programs, while evidence from humid tropical, monsoon, freeze–thaw and low-resource urban contexts is comparatively lacking. As reported in published studies, well-designed phytocaps can result in reduced percolation compared to traditional clay caps. Reported publications also mention considerable construction-cost savings, depending on site conditions and design assumptions. Methane-related outcomes vary by measurement method and site context, with studies reporting surface flux reductions, methane oxidation and landfill gas attenuation as distinct performance indicators. These advantages are counter-balanced by design uncertainties that vary from site to site, limited long-term monitoring data, climate transferability concerns, and regulatory systems still firmly anchored in prescriptive low-permeability barriers. This review proposes a policy-oriented analytical framework that bridges the gap between technical performance evidence, urban co-benefits, staged monitoring and performance-based landfill closure regulation. As such, phytocapping should be considered not as a general-purpose substitute for engineered covers, but as a climate-responsive nature-based solution that can complement urban waste servicing infrastructure, ecological restoration and adaptive governance of landfills when properly designed, monitored and regulated. Full article

Studies on the association between green spaces and childhood allergic diseases are limited and have yielded inconsistent results across different regions. Furthermore, this relationship remains underexplored in Chengdu, China. In this study, we aimed to investigate the association between the residential greening rate, Normalized Difference Vegetation Index (NDVI), proportion of allergenic plants (proportion of allergenic woody plants, proportion of allergenic herbaceous plants), the closeness-to-nature characteristics of children’s playgrounds, and childhood allergic diseases in Chengdu. Seven representative neighborhoods were selected based on a 2018 database of pediatric patients from West China Hospital of Sichuan University. Through questionnaires in 2025, data on allergic diseases (e.g., atopic dermatitis and allergic rhinitis) were collected for 210 children aged 0–6 years. Logistic regression models were employed to analyze the data. The results indicate that in the overall sample, residential greening rate, NDVI, proportion of allergenic plants and closeness-to-nature characteristics of children’s playgrounds showed no significant association with allergic diseases. However, subgroup analyses revealed that greening rate was positively associated with allergy risk among children aged 0–3 years, whereas the proportion of allergenic woody plants was negatively associated with allergy risk in this age group; residential NDVI was significantly negatively associated with allergy risk in low-to-medium-housing-price neighborhoods (<19,000 CNY/m²), but positively associated in high-housing-price neighborhoods (≥19,000 CNY/m²); the proportion of allergenic herbaceous plants was significantly negatively associated with allergy risk in children only in high-housing-price neighborhoods. The presence of children’s playgrounds, accessibility of natural elements, integration of sites and facilities into the landscape, and use of natural materials in playgrounds and facilities showed a negative trend with allergy risk in low-to-medium-housing-price neighborhoods; the integration of sites and facilities into the landscape was significantly positively associated with allergy risk in high-rise neighborhoods. Collectively, these associations vary based on individual and neighborhood characteristics. Targeted green space planning and design interventions should be context-specific, synergistically optimizing vegetation coverage and plant composition, while enhancing the closeness-to-nature characteristics of children’s playgrounds within neighborhoods. Our results provide empirical evidence that may offer insights into the development of healthy and child-friendly cities. Full article

Urbanisation is reshaping ecosystems and increasing human–wildlife interactions. Wild boar (Sus scrofa), a highly adaptable species, is increasingly common in European cities, where it exploits natural and anthropogenic resources, often leading to conflict. Predicting when and where wild boars enter urban areas remains challenging, particularly using scalable tools such as remote sensing. Here, we show that temporal and spatial drivers of urban presence are decoupled in Barcelona over a 14-year period. Seasonal vegetation dynamics influenced the timing of urban incursions, with peaks in spring and late summer associated with changes in vegetation moisture and likely reinforced by increased energetic demands during reproduction and early lactation. However, remotely sensed vegetation indices captured these dynamics only partially, limiting their predictive power when used alone. Spatial variation in urban green area use was primarily explained by landscape structure, with proximity to streams and habitat fragmentation contributing similarly. Green areas near natural corridors were concentrated higher and had more variable presence, while heterogeneous landscapes likely facilitated repeated use by increasing access to foraging and refuge. Integrating remote sensing with landscape metrics can improve the anticipation and management of human–wildlife conflicts. Full article

Green walls, as part of nature-based solutions, have been implemented in urban environments, enhancing energy efficiency, thermal regulation, biodiversity, environmental quality, and human well-being. Despite these benefits, green walls’ environmental performance across their life cycle is reported inconsistently in the literature, limiting robust comparisons and evidence-based decision-making in the built environment. This review synthesizes current knowledge on the environmental performance of green walls, living wall systems, and active living walls, including systems that improve indoor air quality and enable water reuse. A systematic literature review was conducted following PRISMA guidelines using the databases ScienceDirect, Scopus, and Google Scholar. The results show that methodology gaps in life cycle assessment (LCA) studies of living wall systems restrict their applicability for evidence-based design and specification. Future research should integrate embodied and operational impacts in scenario-based and sensitivity analyses considering plant selection, irrigation strategies, maintenance regimes, replacement rates and service-life assumptions. More focus should be given to tropical cities to understand the impact of climate, water demand, vegetation performance. and maintenance intensity. These improvements would lead to more comparable, context-sensitive, and design-oriented LCA evidence for sustainable building applications. Full article

Urban heat stress and deteriorating air quality are environmental risks in semi-arid cities, positioning urban forests as vital nature-based solutions for climate adaptation. Despite growing recognition of urban greening imperatives, South Africa’s (SA) Northern Cape Province remains characterized by sparse vegetation Land Use/Land Cover (LULC) and built environment expansion. The study’s research problem focuses on how vegetation LULC dynamics influence urban forests’ potential in mitigating heat stress and atmospheric pollution in arid urban systems. The study adopts a multi-scale analytical approach, conducting the LULC and NDVI analysis through a multi-temporal Landsat satellite imagery analysis quantifying LULC change from 2004 to 2024. Grounded in the Integrated Spatial Justice-Ecosystem Services (ISJES) Framework, the analysis reveals significant decline in dense vegetation LULC from 9021.77 km² (2.4%) to 1262.10 km² (0.3%), while barren land expanded from 73,417.01 km² (19.7%) to 222,866.82 km² (59.8%) intensifying urban thermal exposure. Built-up areas expanded from 91.06 km² to 357.072 km², further constraining ecological buffers across the province’s urban nodes and undermining urban climate resilience. The Global Moran’s I statistic for the NDVI change surface (I = 0.7843, Z = 443.87, p < 0.0001) confirms spatial clustering of degradation hotspots of NDVI decline affecting 66.5% of the study area. Furthermore, Geographically Weighted Regression (GWR) results confirm that vegetation loss is being driven by the combined and spatially differentiated effects of mining proximity, urban expansion, livestock pressure, declining rainfall, and rising temperatures. In terms of thermal regulation findings, the Getis-Ord Gi hot spot analysis identifies significant NDVI decline covering 23.5% of the study area at the 99% confidence level, expanding to 33.5% and 39.5% at the 95% and 90% confidence levels, respectively; hence, there is a need for urban forest corridors, climate-sensitive spatial planning frameworks, and targeted greening interventions in heat-vulnerable arid geographies. This study provides the first comprehensive, multi-decadal quantification of vegetation loss across SA’s largest province. Full article

Rice brown spot, caused by Bipolaris oryzae, is an important constraint for rice production and requires timely field-scale monitoring. This study evaluated the use of multispectral bands acquired with a UAV-mounted sensor, together with vegetation indices, combined with machine-learning models to estimate rice brown spot severity under field conditions in Lambayeque and Cajamarca, Peru. A total of 37 sampling observations were collected across the vegetative, flowering, and milk-ripening stages. Spectral variables were extracted from UAV orthomosaics and related to field-based disease severity assessments. The strongest correlations with severity were observed for NDRE (r = −0.83) and NPCI (r = 0.77). Three regression models were evaluated using leave-one-out cross-validation (LOOCV): support vector regression with radial basis function kernel (SVR-rbf), support vector regression with linear kernel (SVR-linear), and Random Forest (RF). The SVR-linear model showed the lowest prediction error using NDRE, GREEN, and BLUE as predictors (R²_CV = 0.76; RMSE_CV = 1.31), although its performance was very similar to that of SVR-rbf and RF. These results indicate that UAV-derived multispectral information can support plot-level estimation of rice brown spot severity. However, model performance should be interpreted cautiously because of the small dataset, heterogeneous disease conditions, and moderate prediction accuracy. Further studies with larger and independent datasets are needed to improve robustness and transferability. Full article

Waterlogging at the tillering stage, a key early vegetative growth stage, is increasingly limiting wheat productivity worldwide, but the physiological mechanisms underlying genotypic tolerance are not fully understood. To address this, 23 bread wheat (Triticum aestivum L.) genotypes from five germplasm groups were exposed to 14 days of waterlogging at the tillering stage. Morphological traits including leaf (green area, biomass, and senescent biomass proportion) and the elongation rate of the main culm (cm day⁻¹), plant water status (relative water content, RWC), photosynthetic pigment content (SPAD values; total chlorophyll, TChl; total carotenoids, TCar), and photosynthetic performance (maximal photochemical efficiency of photosystem II, F_v/Fm; actual photochemical efficiency of photosystem II, F_v′/F_m′; net photosyntheis, P_n; stomatal conductance to water vapor, g_s), were assessed. Waterlogging induced strong but highly variable responses among genotypes. Sensitive genotypes showed marked reductions in green biomass (up to ~40–60%), TChl content (up to ~80%), TCar (~70%), and photosynthetic performance, including declines in F_v/F_m, F_v′/F_m′, and P_n. In contrast, tolerant genotypes maintained higher photochemical efficiency, P_n, and pigment content, despite stress exposure, underscoring greater functional resilience. Importantly, morphological stability did not consistently translate into functional performance. Several genotypes maintained green leaf area despite pronounced declines in photosynthetic capacity and pigment content, revealing a decoupling between morphological and physiological responses. Multivariate analysis identified an integrated photosynthetic trait axis strongly associated with yield performance under stress, highlighting that tolerance is primarily driven by the capacity to maintain photosynthetic function rather than green biomass alone. Together, these findings emphasize the importance of preserving both physiological functionality and green leaf area to maintain waterlogging tolerance. Integrated physiological markers (e.g., TChl and TCar content, photochemical quenching, leaf gas exchange traits) enable effective early screening and support function-based selection in wheat breeding programs. Full article

Efficient nutrient management is essential for sustainable orchard production; however, conventional laboratory analyses used to assess plant nutritional status are time-consuming and expensive. Optical sensing technologies offer a rapid and non-destructive alternative. This study evaluated the potential of proximal optical sensors and UAV-based multispectral imagery to assess the nutritional status of young potted ‘Nashi’ pear (Pyrus pyrifolia (Burm. f.) Nakai) trees. Three fertilization treatments based on different concentrations of Hoagland solution were applied to 18 one-year-old potted trees. Leaf measurements were collected during the growing season using Dualex, CL-01 chlorophyll meter, and Pocket PEA fluorimeter, while UAV-based multispectral imagery was used to calculate vegetation indices, including NDVI, SR, OSAVI, and MSAVI. Leaf nitrogen (N), phosphorus (P), and potassium (K) concentrations were chemically determined and used as reference values for the regression analyses. Significant (p < 0.05) relationships were observed between leaf N content (N%) and several optical parameters related to leaf pigments, including chlorophyll, flavonols, and the Nitrogen Balance Index (NBI), as well as multispectral indices, although with weak associations (R² = 0.326–0.488). The strongest individual relationship with N% was shown by NBI (R² = 0.480). To account for repeated measurements on the same plants, linear mixed-effects models were fitted. These models indicated that NBI showed the strongest association with N% among the proximal optical parameters (β = 0.019, p < 0.001; RMSE = 0.113; MAE = 0.091), followed by flavonols and Dualex chlorophyll. In contrast, optical parameters showed limited sensitivity to P and K. Multispectral indices were not significantly related to K, while only Red and Green reflectance showed weak correlations with P. Overall, optical parameters showed the best associations with N% under the combined nutrient-gradient conditions tested, whereas the assessment of P and K remained limited and should be considered exploratory. Full article